People in public open space can be grouped by their movement trajectories.

Semi-fixed or movable site features are associated with active and playful use of space.

Playful trajectory is associated with higher amount of physical activity in comparison to other trajectory types.

Not enough studies have examined how specific design features of public open space, such as movable site features, are associated with people's physical activity level or playfulness. To fill this gap, this study uses deep learning-based methods to extract visitors' movement trajectories (n = 18,592) from a time-lapse video of a promenade in Hong Kong. The trajectories are classified into different groups based on a set of movement indicators. Multinomial logistic regression is used to examine the relationship between trajectory types and the level of interaction with different site features. A one-way analysis of variance (ANOVA) is also used to compare the average amount of physical activity among different trajectory types. The results show that interaction with semi-fixed or movable site features is associated with higher odds of people having “playful” trajectories than other types of trajectories. People with “sporty” trajectories and “playful” trajectories on average have the highest amount of physical activity.

Physical activity

Play

Public space

Urban design

Deep learning

© 2023 Elsevier Ltd. All rights reserved.